library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.1 ✔ purrr 0.3.3
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ───────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(rvest)
## Loading required package: xml2
##
## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
##
## pluck
## The following object is masked from 'package:readr':
##
## guess_encoding
library(readr)
library(viridis)
## Loading required package: viridisLite
library(leaflet)
knitr::opts_chunk$set(
echo = TRUE,
warning = FALSE,
fig.width = 8,
fig.height = 6,
out.width = "90%"
)
options(
ggplot2.continuous.colour = "viridis",
ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d
theme_set(theme_minimal() + theme(legend.position = "bottom"))
data_2018 =
read_csv("./data/2018data.csv") %>%
janitor::clean_names()
## Parsed with column specification:
## cols(
## .default = col_character(),
## TIME = col_time(format = ""),
## `ZIP CODE` = col_double(),
## LATITUDE = col_double(),
## LONGITUDE = col_double(),
## `NUMBER OF PERSONS INJURED` = col_double(),
## `NUMBER OF PERSONS KILLED` = col_double(),
## `NUMBER OF PEDESTRIANS INJURED` = col_double(),
## `NUMBER OF PEDESTRIANS KILLED` = col_double(),
## `NUMBER OF CYCLIST INJURED` = col_double(),
## `NUMBER OF CYCLIST KILLED` = col_double(),
## `NUMBER OF MOTORIST INJURED` = col_double(),
## `NUMBER OF MOTORIST KILLED` = col_double(),
## COLLISION_ID = col_double()
## )
## See spec(...) for full column specifications.
newnames = colnames(data_2018) %>%
str_replace("number_of_","")
names(data_2018) = newnames
tidy_data =
data_2018 %>%
mutate(
date_complete = date
) %>%
separate(date, into = c("month", "day", "year"), sep = "/") %>%
separate(time, into = c("hour", "minute"), sep = ":") %>%
select(-zip_code, -location, -on_street_name, -cross_street_name, -off_street_name,-collision_id,-year) %>%
rename("vehicle_type" = "vehicle_type_code_1") %>%
mutate( day = as.numeric(day),
month = as.numeric(month),
hour = as.numeric(hour),
minute = as.numeric(minute),
latitude = replace_na(latitude,0),
vehicle_type = str_to_lower(vehicle_type)
) %>%
filter( latitude != 0)
Vehicle type
vehicle_type_data =
tidy_data %>%
mutate(
vehicle_type = replace(vehicle_type,str_detect(vehicle_type,"truck"),"truck"),
vehicle_type = replace(vehicle_type,str_detect(vehicle_type,"sport utility"),"sport utility vehicle")
) %>%
filter( vehicle_type %in% c("taxi","passenger vehicle","truck","sport utility vehicle")) %>%
group_by(vehicle_type,hour) %>%
summarize(
n = n()
)
vehicle_type_data %>%
plot_ly(
x = ~hour, y = ~n, color = ~vehicle_type, type = "scatter", mode = "line") %>%
layout(
title = "Collisions of Day for Different Vehicles",
xaxis = list(title = "Hour of Day"),
yaxis = list(title = "Collisions")
)
Top 8 Collision Reasons
reason_data =
tidy_data %>%
group_by(contributing_factor_vehicle_1) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
head(10)
reason_data %>%
plot_ly(x = ~reorder(contributing_factor_vehicle_1,desc(n)), y = ~n, color = ~contributing_factor_vehicle_1 ,type = "bar") %>%
layout(
title = "The Number of Items Ordered in Each Aisle",
xaxis = list(title = "Different Reasons"),
yaxis = list(title = "Count")
)
Mapping
data_2018 = tidy_data
data_2018 = rename(data_2018, long = latitude, lat = longitude)
pal <- colorNumeric(
palette = "viridis",
domain = data_2018$persons_injured)
data_2018 %>%
filter(!(lat < "-70" | lat >= "-75")) %>%
filter(persons_injured > 2) %>%
mutate(
label = str_c("<b>vehicle type: ", vehicle_type, "</b><br>Month: ", month , sep = "") ) %>%
sample_n(2000) %>%
leaflet() %>%
addTiles() %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addLegend("bottomright", pal = pal, values = ~persons_injured,
title = "Persons Injured",
opacity = 1
) %>%
addCircleMarkers(
~lat, ~long,
color = ~pal(persons_injured),
radius = 0.5,
popup = ~ label)
data_2018 %>%
group_by(borough) %>%
summarise(n())
## # A tibble: 6 x 2
## borough `n()`
## <chr> <int>
## 1 BRONX 22121
## 2 BROOKLYN 46314
## 3 MANHATTAN 29728
## 4 QUEENS 40400
## 5 STATEN ISLAND 5988
## 6 <NA> 71583
data_2018_seperate = tidy_data
data_kill_injured = data_2018_seperate %>%
select(month, persons_injured,persons_killed, pedestrians_injured, pedestrians_killed, cyclist_injured, cyclist_killed, motorist_injured, motorist_killed)
data_kill_injured$injured = apply(data_kill_injured[,c(2,4,6,8)],1,sum,na.rm=T)
data_kill_injured$killed = apply(data_kill_injured[,c(3,5,7,9)],1,sum,na.rm=T)
data_kill_injured =
data_kill_injured %>%
group_by(month) %>%
summarise(
sum_injured = sum(injured),
sum_killed = sum(killed)
) %>%
ungroup()
data_kill_injured = data_kill_injured %>%
pivot_longer(
sum_injured:sum_killed,
names_to = "type",
values_to = "number"
)
plot_kill_injured = data_kill_injured %>%
ggplot(aes(x = month, y = number, color = type))+
geom_point()+
geom_line()+
scale_x_continuous(breaks=seq(1, 12, 1),
labels = c("Jan", "Feb", "Mar", "Apr", "May",
"Jun","Jul", "Aug", "Sep", "Oct",
"Nov", "Dec"))+
scale_y_continuous(breaks = seq(0,11000,1000))+
labs(
title = "Trend of People being Injured or Killed through the Year")+
theme(axis.title = element_text(size=14,face="bold"),
plot.title = element_text(hjust = 0.5,color ="Blue"))
plot_kill_injured = ggplotly(plot_kill_injured)
plot_kill_injured
data_kill_injured_day = data_2018_seperate %>%
select(date_complete, persons_injured,persons_killed, pedestrians_injured, pedestrians_killed, cyclist_injured, cyclist_killed, motorist_injured, motorist_killed)
data_kill_injured_day$injured = apply(data_kill_injured_day[,c(2,4,6,8)],1,sum,na.rm=T)
data_kill_injured_day$killed = apply(data_kill_injured_day[,c(3,5,7,9)],1,sum,na.rm=T)
data_kill_injured_day =
data_kill_injured_day%>%
group_by(date_complete) %>%
summarise(
sum_injured = sum(injured),
sum_killed = sum(killed)
) %>%
ungroup()
data_kill_injured_day = data_kill_injured_day %>%
pivot_longer(
sum_injured:sum_killed,
names_to = "type",
values_to = "number"
) %>%
mutate(
day = rep(1:365, each = 2),
month = rep(1:12,c(62,56,62,60,62,60,62,62,60,62,60,62))
)
plot_kill_injured_day = data_kill_injured_day %>%
ggplot(aes(x = date_complete, y = number, group = type, color = type))+
geom_line()+
theme(axis.text.x = element_blank(),
axis.title = element_text(size=14,face="bold"),
plot.title = element_text(hjust = 0.5,color ="Blue"))+
labs(
title = "Trend of People being Injured or Killed through the Day",
x = "Day of the Year")
plot_kill_injured_day
plot_kill_injured_day = ggplotly(plot_kill_injured_day)
plot_kill_injured_day
proportion of accident and injured people by borough, by hour
boro_plot = tidy_data %>%
drop_na(borough, persons_injured, persons_killed) %>%
group_by(borough) %>%
summarize(accident = n(),
injured = sum(persons_injured)) %>%
mutate(prop_accident = round(accident/sum(accident),4),
prop_injured = round(injured/sum(injured),4)) %>%
mutate(text_label1 = str_c("accident: ", accident),
text_label2 = str_c("injured: ", injured)) %>%
plot_ly(x = ~borough, y = ~prop_accident, type = 'bar', name = 'accident', text = ~text_label1, alpha = 0.8) %>%
add_trace(x = ~borough, y = ~prop_injured, type = 'bar', name = 'injury', text = ~text_label2, alpha = 0.8) %>%
layout(title="proportion of accident and injured people by borough", xaxis = list(title = 'borough'), barmode = 'group', legend = list(orientation = "h", xanchor = "center", x = 0.5, y = -0.2))
boro_plot
Number of accident and injured people in Brooklyn is greatest comparing with other boroughs. Queen is second. Staten Island have fewest accident and injured people. Manhattan and Bronx is in the middle.
hour_table = tidy_data %>%
drop_na(hour, persons_injured, persons_killed) %>%
group_by(hour) %>%
summarize(accident = n(),
injured = sum(persons_injured))
hour_plot = hour_table %>%
mutate(prop_accident = round(accident/sum(accident),4),
prop_injured = round(injured/sum(injured),4)) %>%
mutate(text_label1 = str_c("accident: ", accident),
text_label2 = str_c("injured: ", injured)) %>%
plot_ly(x = ~hour, y = ~prop_accident, type = 'scatter', mode = 'lines', name = 'accident', text = ~text_label1, alpha = 0.8) %>%
add_trace(x = ~hour, y = ~prop_injured, type = 'scatter', mode = 'lines', name = 'injury', text = ~text_label2, alpha = 0.8) %>%
layout(title="proportion of accident and injured people by hour", xaxis = list(autotick = FALSE, ticks = "outside", tick0 = 0, dtick = 1, title = 'Hour'), yaxis = list(autotick = FALSE, ticks = "outside", tick0 = 0, dtick = 0.01, title = 'proportion'), legend = list(orientation = "h", xanchor = "center", x = 0.5, y = -0.2))
hour_plot %>%
layout(shapes = list(type = "rect", fillcolor = "pink", line = list(color = "pink"), opacity = 0.3, x0 = 8, x1 = 19, xref = "x", y0 = 0, y1 = 0.08, yref = "y"))
Frequency of accident and injured people in the period of 8 am-19 pm is higher than other period of time. Started from 3 am, number of accident and injured people increase and reach a small peak at 8am. At 5pm, it reaches a big peak. then it started to decrease.